「stat」の検索結果

Assemble a wordless macro-tabletop food-product sizzle ad from a config — normalize fps and SAR across ~4 photorealistic macro clips (hands tearing, flat lay, bite, box hero), concat them, apply a global anti-AI grain pass (eq plus hqdn3d plus noise), composite the audio (a non-diegetic instrumental music bed plus a couple of short diegetic SFX like a snap and a tear placed at measured cue points, loudnorm), composite a STATIC end card entirely in PIL (real logo PNG plus real product PNG plus a serif heritage headline plus a CTA — never AI-rendered text), and burn optional serif stat-callout pills at beats. This is the FREE deterministic assembly stage (normalized concat plus grain plus music and SFX mix plus PIL end card plus callouts); the macro keyframes, i2v clips, and music bed come from create-image-fal, create-video-fal, and create-music-elevenlabs. Use for the food-product-sizzle format.

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Orchestrate Project Osmos for long-running Fabric and OneLake data-engineering outcomes, including task status, messages, follow-ups, cancellation, and deletion. Osmos tasks are remote service tasks, not local background agents. In Fabric portal Copilot, direct interaction is unsupported; point users to GitHub Copilot CLI. Use spark-cli for direct non-Osmos Spark work. Triggers: "Project Osmos", "Project Osmos task status", "check my Osmos task".

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Builds and edits Simulink, System Composer, Stateflow, and Simscape models. Use when modifying model structure, parameters, ports, connections, or Stateflow chart internals.

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Unsupervised clustering and cell-type identification for high-dimensional flow, spectral, and mass cytometry - FlowSOM, PhenoGraph, FlowSOM-via-CATALYST, with UMAP/tSNE for visualization. Covers the type-vs-state marker distinction (cluster on lineage, test state within clusters), over-provision-then-metacluster, the Weber-Robinson benchmark, seed dependence and metacluster stability, why embeddings are for looking not measuring, and median-heatmap annotation/merging. Use when discovering populations without predefined gates, choosing a clustering algorithm, selecting the number of metaclusters, or annotating clusters into cell types.

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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).

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Detect introgression and admixture between species or populations using Dsuite (Malinsky 2021 fast D-statistics), Patterson's D / ABBA-BABA test (Green 2010; Durand 2011), f4-ratio and f-branch statistic (Malinsky 2018), TreeMix (Pickrell & Pritchard 2012), HyDe (Blischak 2018), QuIBL (Edelman 2019), sprime (Browning 2018), Twisst (Martin 2017), PhyloNet (Than 2008) for explicit phylogenetic networks, and qpAdm / qpGraph (Patterson 2012). Distinguish introgression from incomplete lineage sorting (ILS), ancestral structure, ghost-lineage admixture, and rate variation. Use when testing inter-species gene flow, dating admixture events, identifying introgressed segments, building phylogenetic networks for reticulate evolution, or applying the ABBAclustering (Koppetsch-Malinsky-Matschiner 2024) framework for divergent-species gene flow.

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Quantify homologous recombination deficiency (HRD) from tumor copy number using the three genomic-scar metrics — loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI) — with scarHRD, and via the whole-genome HRDetect and CHORD models. Covers the genomic instability score, the PARP-inhibitor clinical context, whole-genome-doubling correction, and the scar-versus-state distinction. Use when computing an HRD score for PARP-inhibitor eligibility, deriving LOH/LST/TAI scars from allele-specific copy number, deciding between scar-based and mutational-signature HRD methods, or interpreting an HRD result in a BRCA-reverted or low-purity tumor.

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Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.

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Prepares statistical reports for clinical trials following CONSORT 2025, SPIRIT 2025, ICH E9(R1) estimands, and FDA 2023 covariate adjustment guidance. Covers Table 1 generation, analysis populations (ITT/FAS/PP/Safety), the 5 ICH E9(R1) intercurrent-event strategies, MMRM under MAR (mmrm), reference-based MI (rbmi J2R/CR/CIR), Permutt tipping-point sensitivity, and Rubin's-rules vs frequentist variance debate. Use when preparing regulatory submissions, defining estimands, or implementing missing-data sensitivity analyses.

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Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.

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Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.

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Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.

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Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.

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Analyzes code files and reports statistics including line counts, file counts by extension, and total size. Use this skill whenever the user wants to understand the composition of a codebase - asking about "how many lines of code", "what file types exist", "code distribution", or needing a quick audit of project size and structure. Make sure to invoke this skill when users mention analyzing codebases, counting lines, checking file distributions, or auditing code.

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Use when the user wants to modernize iOS code to iOS 17/18 patterns, migrate from ObservableObject to @Observable, update @StateObject to @State, or adopt modern SwiftUI APIs.

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Implement, review, or improve SwiftUI Liquid Glass effects for iOS 26+. Covers glassEffect modifier, GlassEffectContainer, glass button styles, glass toolbar/tab bar, static status badges vs interactive controls, morphing transitions, tinting, interactive glass, ToolbarSpacer, scrollEdgeEffectStyle, backgroundExtensionEffect, and availability gating. Use when asked about Liquid Glass, glass buttons, glassEffect, GlassEffectTransition, glassEffectID, glassEffectUnion, scroll edge effects, or adopting iOS 26 design.

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Implement, review, or improve SwiftUI gesture handling. Use when adding tap, long press, drag, magnify, or rotate gestures, composing gestures with simultaneously/sequenced/exclusively, managing transient state with @GestureState, resolving parent/child gesture conflicts with highPriorityGesture or simultaneousGesture, building custom Gesture protocol conformances, or migrating from deprecated MagnificationGesture to MagnifyGesture or using the newer RotateGesture.

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Implement, review, or improve in-app purchases and subscriptions using StoreKit 2. Use when building paywalls with SubscriptionStoreView or ProductView, processing transactions with Product and Transaction APIs, verifying entitlements, handling purchase flows (consumable, non-consumable, auto-renewable), implementing offer codes or promotional/win-back/introductory offers, managing subscription status and renewal state, setting up StoreKit testing with configuration files, or integrating Family Sharing, Ask to Buy, refund handling, and billing retry logic.

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Read, write, and query Apple Health data using HealthKit. Covers HKHealthStore authorization, sample queries, statistics queries, statistics collection queries for charts, saving HKQuantitySample data, background delivery, workout sessions with HKWorkoutSession and HKLiveWorkoutBuilder, HKUnit, and HKQuantityTypeIdentifier values. Use when integrating with Apple Health, displaying health metrics, recording workouts, or enabling background health data delivery.

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Reads from, writes to, and manages relational databases using MATLAB Database Toolbox. Use when connecting to databases, reading data with sqlread or fetch, filtering with rowfilter, writing with sqlwrite, updating with sqlupdate, executing SQL statements, managing transactions with commit and rollback, mapping MATLAB classes to tables with ORM (Mappable, ormread, ormwrite, ormupdate), or performing any database operation from MATLAB. Triggers on: database, SQL, sqlread, sqlwrite, sqlupdate, fetch, execute, rowfilter, RowFilter, ORM, Mappable, ormread, ormwrite, ormupdate, orm2sql, transaction, commit, rollback, Database Toolbox, PostgreSQL, MySQL, SQLite, SQL Server, Oracle, database connection, database table, query database, insert data, update rows, delete rows, stored procedure, prepared statement, odbc, databaseConnectionOptions, datasource, data source, DSN, connection string, multithreaded, parallel.

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Foundation skill for all RoadRunner workflows: MATLAB path setup, connection, project/scene/scenario lifecycle, world settings, handle management, status, and close. Use when connecting to RoadRunner, managing projects/scenes/scenarios, setting world origin, checking status, closing RoadRunner, or when any downstream RoadRunner skill needs initialization.

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Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).

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CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools. Use when deploying database, functions, app, hosting, or gateway resources described in cloudbaserc.json/yaml as a single desired-state config, when a user wants to build static hosting artifacts locally first (deployBuild), or wants a dry-run plan before applying (deployPlan), or when handling multi-environment deploys via mode / envOverrides. Covers build-plan-apply flow (deployBuild local build → deployPlan dry-run → deployApply confirm=true), hosting build-output neutralization, envId resolution priority, only/skip filtering, concurrency, and continueOnError. Prefer deployBuild (when hosting declares a buildCommand) and deployPlan before deployApply; do not confuse with per-resource tcb CLI deploy or single-function deploy.

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